Intern Market Risk Modelling (m/f/d)*

Modelling means simplifying a complex issue in a way that the respective questions can be analysed quantitatively and answered with reasonable approximation.

Within the Integrated Risk Management (IRM) division, IRM1.2.2 is responsible for modelling financial market and credit risks. This includes developing and continuously refining methodology and models used to analyse Munich Re's financial risks.

Munich Re's investment portfolio exceeds €200 billion and is invested globally across a broad range of asset classes such as bonds, equities, derivatives and infrastructure investments. The central question of our work is to quantify the level of risk within this investment portfolio, a question that is crucial both for internal decision-making and external communication. Above all, it is an engaging topic that provides insight into many areas of the company, asset management and current financial market developments. The right tools are essential for generating meaningful insights, and we continuously improve and develop them as part of our work.

We are looking for an intern to support our team for a minimum of two months full time followed by two months part time with the possibility of extension. This role provides the chance to work on challenging and impactful topics being closely integrated within our team. We will support and guide you, and you will participate in our team activities. You will be able to apply your programming skills and to learn about numerous applications of financial mathematics to assets modelling and risk management. At the same time you will interact with interns and employees of other departments and gain insights in various areas of the company.

Your Job

• Help us develop and enhance our analysis tools for market risk
• Bring in your own ideas for improving processes, tools or analytical approaches
• Analyse financial market data and derive insights for our risk models
• Collaborate closely with team members to solve modelling and data challenges

Your Profile

• A masters student with excellent academic track record in a quantitative field like mathematics, physics, data science or related
• Ability to structure complex problems and develop appropriate solutions
• Curiosity to learn about mathematical finance and modelling; previous experience is a plus
• Self-responsible working style and getting things done attitude
• Solid programming skills needed, preferably in python; data bricks/power BI experience is a plus

About us

As the world's leading reinsurance company with more than 16,000 employees at over 50 locations, Munich Re introduces a paradigm shift in the way you think about insurance. By turning uncertainty into a manageable risk we enable fundamental change. Join us working on topics today that will concern society tomorrow, whether that be climate change, major construction projects, medical risk assessment or even space travel.

Together we embrace a culture where multiskilled teams dare to think big. We create the new and the different for our clients and cultivate innovation.

Sounds like you? Push boundaries with us and be part of Munich Re. Our employees are our greatest strength. That's why we offer them a wide range of benefits. You can find some examples below.

Unlock your potential

• Diversity, Equity & Inclusion: we embrace the power of differences and are convinced that diversity fosters innovation and resilience and enables us to act braver and better.
• Continuous Learning: we believe that continuous learning is a key differentiator and critical for building new skills and accelerating growth.
• Career Mobility: we actively support career mobility, and our strong global and regional presence offers a wealth of career growth opportunities for you.

Münchener Rückversicherungs-Gesellschaft

Silke Rößler / Oksana Kern / Nina Hartmuth • Coordination Students Program

* Munich Re not only stands for fairness with regard to its clients; it is also an equal opportunity employer. Severely disabled candidates will also be prioritised, if equally qualified.